CT-AI Exam Topics | Pass-Sure CT-AI: Certified Tester AI Testing Exam

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 2
  • ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 3
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 4
  • ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 5
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 7
  • systems from those required for conventional systems.
Topic 8
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 10
  • Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 11
  • Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.

>> CT-AI Exam Topics <<

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)

Answer: C

Explanation:
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network- specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered
"covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.
Options A and B (Sign-Sign and Sign-Change coverage) are more rigid structural criteria and are not described as adaptable within the syllabus. They focus on sign patterns of neuron activations and do not allow altering thresholds. Option D, neuron coverage, measures the proportion of neurons activated at least once.
Although simple, it is not defined as an adaptable criterion. Its limitations are documented: it provides shallow insight and too easily achieves high coverage.
Onlythreshold coverageallows testers to adjust activation thresholds for more refined coverage measurement, makingOption Cthe correct choice.


NEW QUESTION # 157
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

Answer: C

Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)


NEW QUESTION # 158
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)

Answer: A

Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.


NEW QUESTION # 159
Which statement about the property of the test environment for an AI-based system is correct?

Answer: B

Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.


NEW QUESTION # 160
A company is using a spam filter to attempt to identify which emails should be marked as spam.
Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks. How could EDA be used to detect this attack?

Answer: C

Explanation:
The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."


NEW QUESTION # 161
......

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